• Login
    View Item 
    •   MUT Research Archive
    • Journal Articles
    • School of Computing and IT (JA)
    • Journal Articles (CI)
    • View Item
    •   MUT Research Archive
    • Journal Articles
    • School of Computing and IT (JA)
    • Journal Articles (CI)
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    A Comparative Study of Transformer-based Models for Hate-Speech Detection in English-Kiswahili Code-Switched Social Media Text

    Thumbnail
    View/Open
    ijatcse011352024.pdf (367.7Kb)
    Date
    2024
    Author
    Ng’ang’a, Njung’e Fredrick.
    Oirere, Aaron M.
    Ndung'u, Rachel N.
    Metadata
    Show full item record
    Abstract
    The transformer architecture, first introduced in 2017 by researchers at Google, has revolutionized natural language processing in various tasks, including text classification. This architecture formed the basis of future models such as those used in hate speech detection in code-switched text. In this research, we conduct a comparative study of transformer-based models for hate speech detection in English-Kiswahili code-switched text. First, the models were compared as feature extractors using a traditional classifier and then as end-to-end classifiers. The three multilingual transformer-based models compared include mBERT, mDistilBERT and XLM-RoBERTa, using SVM as the traditional classifier for the extracted features. The HateSpeech_Kenya dataset, sourced from Kaggle, was utilized in this study. As a feature extractor, mBERT’s hidden states trained the highest-performing SVM with an accuracy of 0.5461 and a macro f1 score of 0.40. Among the three models evaluated, XLM-RoBERTa achieved the highest accuracy of 0.6069 and a macro f1 score of 0.49 on a balanced dataset. In contrast, mBERT achieved the highest accuracy of 0.7820 and a macro f1 score of 0.53 on an imbalanced dataset. The comparative study establishes that using transformer-based models as end-to-end classifiers generally performs better than using them as feature extractors with traditional classifiers. This is because directly training the models allows them to learn more task-specific features. Furthermore, the varying performance across balanced and imbalanced datasets highlights the need for careful model selection based on the dataset characteristics and specific task requirements.
    URI
    http://repository.mut.ac.ke:8080/xmlui/handle/123456789/6483
    Collections
    • Journal Articles (CI) [118]

    MUT Library copyright © 2017-2024  MUT Library Website
    Contact Us | Send Feedback
     

     

    Browse

    All of Research ArchiveCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    MUT Library copyright © 2017-2024  MUT Library Website
    Contact Us | Send Feedback